Readme updated

#'

#' @param seu_obj A complete Seurat object

#' @param feature Gene name. Only one gene is allowed.

#' @param celltypes Cell types to be included in the dot plot. Default: all cell types.

#' @param groupby The group to show on x axis. One of the column names in meta.data.

#' @param splitby The group to separate the gene expression. One of the column names in meta.data.

#' @param scale.by Methods to scale the dot size. "radius" or "size"

complex_dotplot_single <- function(

  seu_obj, 

  feature, 

  celltypes=NULL,

  groupby,

  splitby=NULL,

  strip.color=NULL,

    seu_obj@meta.data[,groupby] <-factor(seu_obj@meta.data[,groupby], levels = names(table(seu_obj@meta.data[,groupby])))

  }

  levels(seu_obj)<-rev(levels(seu_obj))

  if(is.null(celltypes)){

    celltypes<-levels(seu_obj)

  }

  seu_obj<-subset(seu_obj,idents=celltypes)

  celltypes<-gsub("_", "-", celltypes)

  seu_obj@meta.data$celltype<-as.character(seu_obj@active.ident)

  seu_obj@meta.data$celltype<-gsub("_", "-", seu_obj@meta.data$celltype)

#'

#' @param seu_obj A complete Seurat object

#' @param features A vector of gene names.

#' @param celltypes Cell types to be included in the dot plot. Default: all cell types.

#' @param groupby Group ID Must be one of the column names in the meta.data slot of the Seurat object.

#' @param strip.color Colors for the strip background

#' @return A ggplot object

complex_dotplot_multiple <- function(

  seu_obj, 

  features, 

  celltypes=NULL,

  groupby, 

  strip.color = NULL

  ){

 pb <- progress_bar$new(

   format = "  Ploting [:bar] :percent eta: :eta",

   clear = FALSE, total = length(features), width = 100)

 features=rev(features)

 plot_list<-list()

 for(i in 1:length(features)){

  pp<-invisible(

    complex_dotplot_single(seu_obj = seu_obj, feature = features[i], groupby = groupby)

    complex_dotplot_single(seu_obj = seu_obj, feature = features[i], groupby = groupby, celltypes = celltypes)

  )

  pp<-pp$data

  pp$gene <- features[i]

  Sys.sleep(1 / length(features))

  }

  all_data<-do.call('rbind', plot_list)

  all_data$gene<-factor(all_data$gene, levels = rev(features)) 

  all_data$celltype <- factor(all_data$celltype, levels = levels(seu_obj))

  p <- invisible(

    ggplot(all_data, aes(x = groupby, y = gene)) +  

cluster_colors<-rand_color(length(levels(iri.integrated)))

group_colors<-rand_color(length(table(iri.integrated$Group)))

rep_colors<-group_colors<-rand_color(length(table(iri.integrated$orig.ident)))

###plot and save figures

png(filename =  'circlize_plot.png', width = 6, height = 6,units = 'in', res = 300)

plot_circlize(circ_data,do.label = T, pt.size = 0.01, col.use = cluster_colors ,bg.color = 'white', kde2d.n = 200)

![alt text](https://github.com/HaojiaWu/Plot1cell/blob/master/circlize_plot.png) <br />

### 2. Dotplot to show gene expression across groups

Here is an example to use plot1cell to show one gene expression across different cell types breakdown by group.

```

png(filename =  'dotplot_single.png', width = 4, height = 6,units = 'in', res = 300)

complex_dotplot_single(seu_obj = iri.integrated, feature = "Havcr1",groupby = "Group")

dev.off()

```

![alt text](https://github.com/HaojiaWu/Plot1cell/blob/master/dotplot_single.png) <br />

plot1cell allows visualization of multiple genes in dotplot format too. Here is an example.

```

png(filename =  'dotplot_multiple.png', width = 10, height = 4,units = 'in', res = 300)

complex_dotplot_multiple(seu_obj = iri.integrated, features = c("Slc34a1","Slc7a13","Havcr1","Krt20","Vcam1"),groupby = "Group", celltypes = c("PTS1" ,   "PTS2"  ,  "PTS3"  ,  "NewPT1" , "NewPT2"))

dev.off()

```

![alt text](https://github.com/HaojiaWu/Plot1cell/blob/master/dotplot_multiple.png) <br />

### 3. Vlnplot to show gene expression across groups